pvals.fnc {languageR} | R Documentation |
This function calculates p-values and HPD intervals for the
parameters of models fitted with lmer
. For the fixed-effects
parameters, anticonservative p-values based on the t statistic with
the upper bound for the degrees of freedom are added.
pvals.fnc(object, nsim = 10000, ndigits = 4, withMCMC = FALSE, addPlot=TRUE, ...)
object |
An lmer or glmer model object
fitted with lmer . Currently, MCMC sampling for generalized
linear mixed models may not work off the shelf when there is more
than one random intercept in the model. The code for random effects
structure with correlation parameters is not yet implemented in
mcmcsamp , this currently blocks calculation of the fixed and
random subtables. |
nsim |
An integer denoting the required number of Markov chain Monte Carlo samples. |
ndigits |
An integer denoting the number of decimal digits in the output. |
withMCMC |
A logical indicating whether the output of mcmcsamp
should be returned. |
addPlot |
If TRUE (default), a trellis graph for the posterior distributions of the parameters is shown on the current graphics device. |
... |
Optional arguments that can be passed down. |
A list with components
fixed |
A data frame with the estimated coefficients, their MCMC mean, the HPD 95 and the probability based on the t distribution with the number of observations minus the number of fixed-effects coefficients as degrees of freedom. This last p-value is anti-conservative, especially for small data sets. |
random |
A table with the MCMC means and HPD 95 for the standard deviations and correlations of the random effects. The HPD intervals are based on the untransformed values (log for standard deviations, atanhs for correlations), the endpoints of the HPD interval are back-transformed to the scale of the input data frame for ease of interpretation. |
mcmc |
The output of mcmcsamp . |
R. H. Baayen
See also lmer, mcmcsamp, and HPDinterval.
## Not run: data(primingHeid) library(lme4, keep.source=FALSE) # remove extreme outliers primingHeid = primingHeid[primingHeid$RT < 7.1,] # fit mixed-effects model primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition + (1|Subject) + (1|Word), data = primingHeid) mcmc = pvals.fnc(primingHeid.lmer, nsim=10000, withMCMC=TRUE) mcmc$fixed mcmc$random ## End(Not run)